Semiconductor Yield Predictor
Machine Learning | 2025
Catch the wafers that fail — even when failure is six percent.
End-to-end ownership: data audit, preprocessing pipeline, L1 feature selection, model comparison across five candidates, threshold tuning, and a Streamlit operator UI with a live recall / false-alarm knob. The deliverable is a single-line decision — PASS or FAIL with calibrated probability — that an operator can trust, set their own threshold against, and re-tune as their fab's cost asymmetry shifts.
A four-stage pipeline serialized to joblib artifacts — imputer, scaler, L1 selector, Random Forest — plus a Streamlit UI that loads the artifacts at boot and exposes the decision threshold as a live slider. Notebook for training; app.py for inference; nothing more.
Trained, evaluated, and packaged with the deployed secom/app/app.py Streamlit demo. The final Random Forest + L1 model beats four baselines (incl. XGBoost + SMOTE) on the metric that actually matters for a fab: recall on the failure class.
Five models, one metric that matters: recall on the rare class.
On a dataset where ~6% of wafers fail and 14 out of 15 are good ones, classifiers that look great on accuracy are useless — you can predict “PASS” for every wafer and hit 94%. The right question is how many of the actual failures did you catch? Final model catches 76% at a 0.35 threshold, against a naive-LogReg baseline of 14%.
| Model | Recall | Precision | F1 | ROC-AUC |
|---|---|---|---|---|
| Naive Logistic Regression | 0.14 | 0.14 | 0.14 | — |
| Cost-Sensitive Logistic Regression | 0.29 | 0.15 | 0.20 | — |
| ROS Logistic Regression | 0.29 | 0.15 | 0.20 | — |
| XGBoost + L1 + SMOTE | 0.57 | 0.15 | 0.24 | — |
| ★ Random Forest + L1 (Ours) | 0.76 | 0.23 | 0.35 | 0.81 |
C=0.1) narrows the input to ~113 statistically relevant sensors before any tree gets built.Semiconductor yield is the difference between a $400 wafer and a paperweight.
Modern fabs run hundreds of sensor streams across every step of wafer fabrication — etch, lithography, deposition, polishing. A failing wafer usually leaves a fingerprint in the sensor data, but the fingerprint is buried inside hundreds of correlated dimensions with most failures concentrated in a handful of them. The SECOM dataset is the canonical public benchmark for exactly this problem.
“A typical wafer fabrication process is a complex sequence of operations. Continuous-valued sensor signals are collected throughout — but only a subset are useful for predicting yield.”
“Equipment reliability and overall equipment effectiveness (OEE) are measured in part by the rate of out-of-spec product.”
“With a 14:1 class imbalance, naive classifiers default to predicting the majority class. Accuracy looks great; recall on the rare class is near zero.”
“Cost asymmetry: a missed defect can cost 10-100x what a false alarm costs, depending on where in the process it escapes.”
A 14:1 class imbalance, 590 sensors, 104 failures.
Five candidates, four design decisions, one operator-ready slider.
Naive baselines.
First sprint was deliberately under-engineered: logistic regression, no class weighting, no oversampling. Got 14% recall on the failure class. The point wasn't to ship this — the point was to anchor the ceiling of “what you get if you don't treat the imbalance.”
Three imbalance fixes — all hit a wall.
Cost-sensitive LogReg (class-weighted), ROS LogReg (random over-sampling), and XGBoost + L1 + SMOTE. Recall climbed — 0.29 / 0.29 / 0.57 respectively — but each in a way that hurts production: XGBoost + SMOTE over-corrected and flagged 80%+ of wafers as failures. Useful recall, unusable precision floor.
L1 → Random Forest with class_weight='balanced'.
The winning recipe. Preprocess (drop constants, impute median, standard-scale), Lasso LogReg (C=0.1) for feature selection (590 → ~113), then RandomForestClassifier(n_estimators=500, class_weight='balanced', max_depth=6). Lands at 0.76 recall · 0.81 ROC-AUC at the 0.35 threshold. No SMOTE — the built-in class weighting handles the imbalance without over-correcting.
The default classification threshold (0.50) optimizes for accuracy. On a 14:1 imbalance, accuracy and recall pull in opposite directions. Lowering the threshold to 0.35 trades a few percentage points of precision for a meaningful jump in recall — exactly the trade you want when a missed defect is more expensive than a false alarm. The Streamlit slider exposes this so the operator can move it themselves; 0.35 is just the calibrated default.
Four serialized stages, one decision.
Every stage of the pipeline lives as its own joblib artifact in models/. The Streamlit app loads the four artifacts at boot, applies them in order to any wafer the operator pastes in, and returns a calibrated probability plus the PASS / FAIL verdict against the live threshold.
# 1. Preprocess
X = drop_constant_features(X) # 590 → ~474
X = SimpleImputer(strategy='median').fit_transform(X)
X = StandardScaler().fit_transform(X)
# 2. L1 feature selection
selector = SelectFromModel(
LogisticRegression(
penalty='l1', solver='liblinear', C=0.1
),
threshold='median'
)
X = selector.fit_transform(X, y) # ~474 → ~113
# 3. Random Forest
clf = RandomForestClassifier(
n_estimators=500,
class_weight='balanced',
max_depth=6,
random_state=42,
)
clf.fit(X, y)
# 4. Tune threshold for recall
proba = clf.predict_proba(X_val)[:, 1]
threshold = 0.35 # cost-asymmetry defaultL1 before RF noise dimensions cause unstable splits; Lasso narrows feature space before trees. RF over XGBoost XGBoost + SMOTE over-corrects, predicting 80%+ failure rate; RF + class_weight handles the 14:1 ratio gracefully at this dataset size. Adjustable threshold no universal cutoff — costs differ per fab. Streamlit slider lets the operator tune live. Recall as the primary metric cost asymmetry: a missed defect is far more expensive than a false alarm.
The operator UI is one slider and one decision.
The Streamlit app at secom/app/app.py opens with the threshold slider front and center, a wafer-input pane, and a verdict tile that switches between green PASS and amber FAIL depending on where P(fail) lands relative to the slider. Below that: a per-feature contribution panel and the historical confusion matrix from the holdout set.
